Video monitoring devices monitor premises for various purposes, including, e.g., security monitoring, infant or elderly monitoring, videoconferencing, etc. The video feed may be constantly or periodically monitored by security personnel, and/or recorded for later review.
The use of motion detection technology that can automatically detect moving events in the video may decrease monitoring costs and improve the efficiency and sensitivity of monitoring by alerting or otherwise providing notice of significant events. When a motion event, such as a person entering a secure area, is detected, the motion detector may begin a video feed, and/or begin recording the video, or otherwise alert security personnel of the potential intrusion.
Motion detection technology has been playing an increasingly important role in the technology of video recording. Recently, small-sized, portable, lightweight smart video recorders that incorporate motion detection technology have been developed.
Traditional motion detection methods can generally be categorized into two groups: methods based on pixel values and methods based on the optical flow of pixels. The methods based on pixel values detect motion information in the video frames by detecting changes in values of a pixel or group of pixels over time. This type of method may include, for example, background subtraction or temporal frame differencing. Background subtraction methods first generate a background image based on historical image data and then detect motion by comparing the current frame with the background image. Temporal frame differencing methods calculate pixel value differences between two or three successive frames and detect motion areas in which the calculated pixel value differences meets or exceeds a threshold.
In methods based on optical flow, a motion vector (i.e., a flow vector) is assigned to each pixel of the image. If there is no moving object, the change in the motion vectors in the whole image is continuous. On the other hand, if there is at least one moving object, the motion vectors of the moving object(s) are different from that of neighboring area. The area(s) of the moving object(s) may be detected based on such differences.
The methods based on pixel values generally rely on less complex algorithms than the methods based on optical flow, but may deliver lower accuracy. On the other hand, although the methods based on optical flow may provide better performance, they usually require hardware with high computational capacity in order to analyze images in real time, which is difficult implemented on small-sized, portable, lightweight video recorders.
Another advantage of the optical flow methods is that false detections from light interference may be reduced or avoided. Methods based on pixel values (including both background subtraction and temporal frame differencing methods) can be very sensitive to image changes due to some irrelevant motion, such as wind, false motion due to light reflections, or other interference, as described below. On the other hand, as described below, optical flow methods may be very sensitive to image noise. In addition, they may require extensive computational resources because of the high complexity of the algorithm. Accordingly, real-time processing video frames using an optical flow method may require specialized hardware.
(1) Changes in Ambient Light
A false alarm may be generated when the ambient light in the view of the video recorder is suddenly changed, for example, when room lighting is turned on or off. To reduce this type of false alarm, traditional technology may determine the areas of the image in which pixel values have changed; if all (or a majority of) the pixel values in the image have changed, such change may be attributed to the ambient lighting and an alert may not be generated. A problem with this approach is that a motion event causing changes in majority of the pixels (e.g., motion close to the video camera) may be missed.
(2) Irrelevant Movement
Traditional motion detection technology may give false alarms for image changes due to irrelevant movement, rather than movement in which the monitor may be interested. For example, images may change due to the motion of trees or plants due to wind. To reduce this type of false alarm, traditional motion detection technology may determine the number of the pixels whose values have changed; if the calculated number is lower than a threshold, an alarm may not be generated. A problem with this approach is that it may miss some relevant motion that causes only a small change in the image, such as a moving object that is far away from the video camera.
(3) Image Noise
While capturing images, an image sensor may generate noise due to many factors, including environmental factors (e.g., light conditions etc.) and hardware limitations (e.g., sensor temperature, etc.). Noise may be distributed across the whole image, and may cause changes that can be misinterpreted as motion. Traditional motion detection technology may generate a false alarm due to such noise.
There remains a need for motion detection technology that can be implemented on small-sized, portable, lightweight video recorders to meet the challenges discussed above.